Exploring Scaled AIC within English closed compounds

Abstract

The Akaike Information Criterion (AIC) is an established goodness-of-fit measure for selecting models in the analysis of empirical data. However, AIC is sensitive to sample size. Author’s previous research has shown that Scaled AIC, i.e. AIC divided by sample size, is an effective tool for assessing model fit and hierarchizing regression models. The present study explores further properties of this variable. The object of investigation are 66 multiple regression models referring to the processing of closed (concatenated) English compounds taken from Gagné et al.’s (2019) Large Database of English Compounds (LADEC). In particular, Scaled AIC is juxtaposed to the English Lexicon Project (ELP) and British Lexicon Project (BLP) as sources of response times, the lexical decision and naming tasks, compound length, and transparency norms. One-way ANOVA, main effects analysis, and non-parametric tests are used as methods. The findings suggest that Scaled AIC is responsive to experimental design, the source of response times, and the lexical decision and naming tasks. At the same time, the results of this study offer empirical support for the validation of methods employed by Gagné et al. (2019).